Recent research works that utilize GAN in the FL setting focus mainly on two lines of works: One is leveraging GAN to perform data recovery attacks. [1]} assumes a malicious client that utilizes the shared model as the discriminator to train the generator in a GAN. Then, the trained generator is used to mimic the training samples of the victim client. [2]} assumes the server is malicious. The malicious server first learns representations of the victim client's data through DLG and then leverages these representations to train the generator, which eventually can generate the victim's private data. Another is to train high-quality GAN across distributed data sources under privacy, efficiency, or heterogeneity constraints. MD-GAN [3]} proposes a system that the server hosts the generator while each client hosts a discriminator. The discriminators communicate with each other in a peer-to-peer fashion for improving computational efficiency. FedGAN [4]} trains a GAN across Non-IID data sources in a communication efficient way, but it may produce biased data. The follow-up work [5]} is able to generate bias-free synthetic datasets using FedGAN by fine-tuning the federated GAN with synthesized metadata. Fed-TGAN [6]} is proposed to learn complex tabular GAN on non-identical clients.
